研究目的
To increase the number of estimable signal sources and solve the problem of direction finding with two-parallel nested arrays by proposing a 2-D DOA estimation algorithm based on sparse Bayesian estimation.
研究成果
The proposed two-parallel nested arrays provide M2 degrees of freedom with 2M sensors, and the 2-D DOA estimation algorithm based on sparse Bayesian learning shows excellent performance with reduced computational complexity and noise. Future work will focus on optimizing array structure to mitigate mutual coupling effects.
研究不足
The study is based on ideal conditions with stationary signals and white Gaussian noise. Practical issues like nonstationary signals, colored noise, mutual coupling between array elements, and channel inconsistencies are not addressed and may affect accuracy.
1：Experimental Design and Method Selection:
The study proposes a two-parallel nested array structure and a 2-D DOA estimation algorithm using sparse Bayesian learning. It involves vectorization, smoothing reconstruction, SVD, and sparse Bayesian estimation to reduce computational complexity and noise.
2：Sample Selection and Data Sources:
Simulated narrowband far-field signal sources with specific directions are used, generated in MATLAB.
3：List of Experimental Equipment and Materials:
A two-parallel nested array with 2M sensors (M elements per subarray), signal sources, and noise models.
4：Experimental Procedures and Operational Workflow:
Data from subarrays are processed through reverse ordering, covariance matrix calculation, vectorization, smoothing, SVD, and sparse Bayesian learning to estimate angles. Monte Carlo simulations are conducted for performance evaluation.
5：Data Analysis Methods:
Root-mean-square error (RMSE) is used to evaluate estimation accuracy, with comparisons to other methods under varying SNR, snapshots, and signal numbers.
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